finance/equity-research/model-update/SKILL.md
# Model Update description: Update financial models with new data — quarterly earnings, management guidance, macro changes, or revised assumptions. Adjusts estimates, recalculates valuation, and flags material changes. Use after earnings, guidance updates, or when assumptions need refreshing. Triggers on "update model", "plug earnings", "refresh estimates", "update numbers for [company]", "new guidance", or "revise estimates". ## Workflow ### Step 1: Identify What Changed Determine the updat
npx skillsauth add harsh040506/claude-code-unified-skill-plugin-library finance/equity-research/model-updateInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
description: Update financial models with new data — quarterly earnings, management guidance, macro changes, or revised assumptions. Adjusts estimates, recalculates valuation, and flags material changes. Use after earnings, guidance updates, or when assumptions need refreshing. Triggers on "update model", "plug earnings", "refresh estimates", "update numbers for [company]", "new guidance", or "revise estimates".
Determine the update trigger:
Update the model with reported actuals:
| Line Item | Prior Estimate | Actual | Delta | Notes | |-----------|---------------|--------|-------|-------| | Revenue | | | | | | Gross Margin | | | | | | Operating Expenses | | | | | | EBITDA | | | | | | EPS | | | | | | [Key metric 1] | | | | | | [Key metric 2] | | | | |
Segment Detail (if applicable):
Balance Sheet / Cash Flow Updates:
Based on the new data, adjust forward estimates:
| | Old FY Est | New FY Est | Change | Old Next FY | New Next FY | Change | |---|-----------|-----------|--------|------------|------------|--------| | Revenue | | | | | | | | EBITDA | | | | | | | | EPS | | | | | | |
Key Assumption Changes:
Recalculate valuation with updated estimates:
| Valuation Method | Prior | Updated | Change | |-----------------|-------|---------|--------| | DCF fair value | | | | | P/E (NTM EPS × target multiple) | | | | | EV/EBITDA (NTM EBITDA × target multiple) | | | | | Price Target | | | |
Estimate Change Summary:
Rating / Price Target:
testing
Performs quality control on single-cell RNA-seq data (.h5ad or .h5 files) using scverse best practices with MAD-based filtering and comprehensive visualizations. Use when users request QC analysis, filtering low-quality cells, assessing data quality, or following scverse/scanpy best practices for single-cell analysis.
tools
Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers include mentions of scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, veloVI, sysVI, scArches, variational autoencoder, VAE, batch correction, data integration, multi-modal, CITE-seq, multiome, reference mapping, latent space.
testing
This skill should be used when scientists need help with research problem selection, project ideation, troubleshooting stuck projects, or strategic scientific decisions. Use this skill when users ask to pitch a new research idea, work through a project problem, evaluate project risks, plan research strategy, navigate decision trees, or get help choosing what scientific problem to work on. Typical requests include "I have an idea for a project", "I'm stuck on my research", "help me evaluate this project", "what should I work on", or "I need strategic advice about my research".
development
Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.